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enumerate() Function Complexity

The enumerate(iterable, start=0) function returns an iterator of (index, item) tuples. It is lazy: building it calls iter(iterable) and consumes nothing, and each step takes one item from that iterator and pairs it with a counter.

n is the number of items taken from the enumerate object. Getting each item from the underlying iterator is priced at O(1), as it is for lists, tuples, strings and ranges; an iterator that does more work per item adds that cost to every step. Each index is priced at O(1), which holds for a start that fits in a machine word.

Complexity Reference

enumerate

Operation	Time	Space	Notes
enumerate(iterable, start=0)	O(1)	O(1)	Calls iter(iterable) once and takes no items; a non-iterable raises TypeError here
next() on an enumerate object	O(1)	O(1)	Takes one item from the underlying iterator
Iterating an enumerate object	O(n)	O(1)	Tuples are produced one at a time, not accumulated
list(enumerate(iterable))	O(n)	O(n)	Holds all n tuples
enumerate[T]	O(1)	O(1)	A generic alias for type hints

Basic Usage

Simple Enumeration

items = ['a', 'b', 'c']

pairs = []
for index, item in enumerate(items):      # O(n)
    pairs.append((index, item))
assert pairs == [(0, 'a'), (1, 'b'), (2, 'c')]

assert list(enumerate(items)) == [(0, 'a'), (1, 'b'), (2, 'c')]   # O(n) time and space

Custom Start Index

start sets the first index. It must be an int, or an object with __index__.

items = ['a', 'b', 'c']

assert list(enumerate(items, start=1)) == [(1, 'a'), (2, 'b'), (3, 'c')]
assert list(enumerate(items, 10)) == [(10, 'a'), (11, 'b'), (12, 'c')]
assert list(enumerate(items, -1)) == [(-1, 'a'), (0, 'b'), (1, 'c')]

try:
    enumerate(items, start=1.5)
except TypeError as exc:
    assert "cannot be interpreted as an integer" in str(exc)
else:
    raise AssertionError("a float start did not raise")

Lazy, Single-Pass Iteration

Building an enumerate object takes no items, and a loop that stops early pays only for the items it took. The object is an iterator over its iterator, so it can be consumed once.

taken = []

def numbers():
    for value in range(10**9):
        taken.append(value)
        yield value

pairs = enumerate(numbers())      # O(1) - nothing is taken yet
assert taken == []

for index, value in pairs:        # O(k) for k items taken
    if index == 3:
        break
assert taken == [0, 1, 2, 3]

assert next(pairs) == (4, 4)      # O(1) - resumes where the loop stopped

pairs = enumerate(['a', 'b'])
assert list(pairs) == [(0, 'a'), (1, 'b')]
assert list(pairs) == []          # already exhausted

try:
    len(enumerate(['a', 'b']))
except TypeError as exc:
    assert "has no len()" in str(exc)
else:
    raise AssertionError("len() did not raise")

try:
    enumerate(['a', 'b'])[0]
except TypeError as exc:
    assert "not subscriptable" in str(exc)
else:
    raise AssertionError("indexing did not raise")

To reverse or index the pairs, build a list first, which costs O(n) time and space:

pairs = list(enumerate(['a', 'b', 'c']))      # O(n)
assert pairs[::-1] == [(2, 'c'), (1, 'b'), (0, 'a')]
assert pairs[1] == (1, 'b')

enumerate() vs range(len(...))

for i in range(len(seq)) with seq[i] needs a sequence that supports len() and indexing, and each seq[i] adds the cost of indexing that sequence. On a list that is O(1), so both loops are O(n). On a deque, indexing away from the ends is O(n), which makes the loop O(n²), while enumerate() walks the deque once. enumerate() also works on generators, files and other iterables that cannot be indexed.

from collections import deque

d = deque(range(1000))

by_index = [(i, d[i]) for i in range(len(d))]    # O(n²) - each d[i] walks the deque
by_enumerate = list(enumerate(d))               # O(n)
assert by_index == by_enumerate

lines = iter(['first', 'second'])
assert list(enumerate(lines, start=1)) == [(1, 'first'), (2, 'second')]

Common Patterns

Finding Every Position

One pass with enumerate() finds every match in O(n). list.index() returns only the first match, and raises ValueError when there is none.

items = ['apple', 'banana', 'cherry', 'banana']

positions = [i for i, item in enumerate(items) if item == 'banana']   # O(n)
assert positions == [1, 3]

assert items.index('banana') == 1     # O(n) - first match only

try:
    items.index('date')
except ValueError:
    pass
else:
    raise AssertionError("index() found a missing item")

Pairing with zip()

names = ['Alice', 'Bob', 'Charlie']
ages = [30, 25, 35]

rows = [f"{i}: {name} is {age}" for i, (name, age) in enumerate(zip(names, ages))]   # O(n)
assert rows == ["0: Alice is 30", "1: Bob is 25", "2: Charlie is 35"]

Building a Lookup Table

items = ['apple', 'banana', 'cherry']

by_number = dict(enumerate(items, start=1))               # O(n)
assert by_number == {1: 'apple', 2: 'banana', 3: 'cherry'}

position = {item: i for i, item in enumerate(items)}      # O(n) once, then O(1) lookups
assert position['cherry'] == 2

Edge Cases

assert list(enumerate([])) == []
assert list(enumerate('ab')) == [(0, 'a'), (1, 'b')]
assert list(enumerate({'x': 1, 'y': 2})) == [(0, 'x'), (1, 'y')]   # dict keys

try:
    enumerate(42)
except TypeError as exc:      # raised when the object is built, not when it is iterated
    assert "not iterable" in str(exc)
else:
    raise AssertionError("enumerate(42) did not raise")

Performance Best Practices

✅ Do:

• Use enumerate() when you need both the index and the item - one O(n) pass over any iterable
• Use break to stop early - only the items taken are paid for
• Build an {item: index} dict once when you look up positions of distinct, hashable items repeatedly

❌ Avoid:

• range(len(seq)) with seq[i] on a deque or another sequence with costly indexing - O(n²)
• list(enumerate(...)) when one loop over the pairs is enough - O(n) space for nothing
• Iterating an exhausted enumerate object again - the second pass is empty

Related Functions

• zip() - Combine multiple iterables
• range() - Generate number sequences
• iter() - Create iterators
• next() - Get next item from iterator

Version Notes

• All Python 3: enumerate() is lazy and single-pass, and its bounds are the same on every supported version